These teachings relate generally to methods and systems for making image-derived information available to enable analyses with semantic annotations accessible using semantic web technology for personalized medicine and discovery science.
Despite high levels of diagnostician competency levels, accurate assessment of cardiovascular disease, cancer, and other disease categories often rely on relatively simple observations as standard of care. From its inception, imaging has allowed visualization of the in vivo characteristics of disease. Increasingly incisive clinical insights are possible and image analysis methods are continuously developed to implement them, yet the increasing capability requires ever more sophisticated computational techniques to exploit.
Imaging, particularly with safe and non-invasive methods, represents the most powerful methods for locating the disease origin, capturing its detailed pathology, directing therapy, and monitoring progression to health. Imaging is also an extremely valuable and low cost method to mitigate human and financial costs by allowing for appropriate early interventions that are both less expensive and disruptive.
Quantitative imaging techniques are developed for use in the clinical care of patients and in the conduct of clinical trials. In clinical practice, quantitative imaging may be used to detect and characterize disease before, during, and after a course of therapy, and used to predict the course of disease.
Quantitative imaging assessment of phenotype implemented in an architecture which proactively optimizes interoperability with modern clinical IT systems provides power to the clinician as they manage their patients across the continuum of disease severity for improved patient classification across surgical, medical, and surveillance pathways. More timely and accurate assessments yield improved outcomes and more efficient use of health care resources, benefits that far outweigh the cost of the tool—at a level of granularity and sophistication closer to the complexity of the disease itself rather than holding the assumption that it can be simplified to a level which belies the underlying biology.
With newer high resolution imaging techniques, unaided, the radiologist would “drown” in data. Integrating quantitative imaging for individual patient management will require a new class of decision support informatics tools to fully exploit the capabilities of these new tools within the realities of existing work flows.
Ex vivo biomarkers (e.g., genomic, proteomic, etc.) as well as in vivo biomarkers (e.g., imaging) are of particular interest in drug development for their potential to accelerate the drug development pipeline. Various collaborative efforts have been established to coordinate efforts in biomarker discovery and development. On the material side, numerous biobanks (e.g., Karolinska Institute Biobank, British Columbia BioLibrary) store patient tissue and fluid samples that can later be allotted for ex vivo biomarker research. In addition to biological samples, probes and tracers can also be banked. The Radiotracer Clearinghouse has been developed to broker the sharing of Positron Emission Tomography (PET) and Single Positron Emission Computed Tomography radiotracers between stakeholders for in vivo biomarker research. On the information side, various databases store information on ex vivo biomarkers (e.g., Early Detection Research Network Biomarker Database, Infectious Disease Biomarker Database). However, information resources for in vivo biomarkers, specifically quantitative imaging biomarkers, are notably lacking.
Quantitative imaging techniques also have potential applications in translational research. In clinical research, quantitative imaging biomarkers are used to define endpoints of clinical trials. There is a large and growing body of knowledge at the molecular/cellular and organism level enabling quantitative imaging techniques in computer-aided detection, diagnosis, and targeted therapies. Technology linking these levels through the analysis of quantitative imaging and non-imaging data, coupled with multi-scale modeling elucidates both pre-symptomatic and clinical disease processes. Although there is great value in application of quantitative imaging techniques in translational research, few technologies facilitate bridging the two bodies of knowledge; at the molecular/cellular level and at the organism level.
Statistical hypothesis testing is usually stated along with a characterization of variability under defined scenarios. Determining the clinical relevance of a quantitative imaging readout is a difficult problem. It is important to establish to what extent a biomarker reading is an intermediate endpoint capable of being measured prior to a definitive endpoint that is causally rather than coincidentally related. A logical and mathematical framework is needed to establish how extant study data may be used to establish performance in contexts that have not been explicitly tested.
However, existing capabilities only rarely relate the logical world of ontology development with the biostatistical analyses that characterize performance. In general, existing tools do not permit the extrapolation of statistical validation results along arbitrary ontology hierarchies. Despite decades of using statistical validation approaches, there is no methodology to formally represent the generalizability of a validation activity.